20 December, 2020

Planning

  • Introduction
  • Crime in SF
  • Transportation interaction
  • COVID 19
  • Explaining crime with socio-demographic data

Research Questions

  1. Given the organization of public transportation in the city, can we say that the places with the most crime are those that are the most connected to everywhere else?

  2. Has the COVID-19 crisis changed any tendencies concerning crime in San Francisco?

  3. Can socio-economic factors such as education, income, ethnicity of the population, poverty, unemployment rate, population density explain intra-city differences between San Francisco’s neighborhoods?

Introduction

  • Data Source :
    • Crime Data: ~2’570’000 obs.
    • San Francisco Neighborhood Socio-Economic Profiles : 41x3 obs.
    • COVID cases in SF : 817 obs.

Crime in SF

Crime in SF

Distribution of Crime

Crime Resolution

Research question 1

4.1 Research question 1

#> 
#> Call:
#> lm(formula = `Total crimes` ~ `number of stops`, data = crime_demostats_2016)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#>  -3606  -2169  -1348    -16  14044 
#> 
#> Coefficients:
#>                   Estimate Std. Error t value Pr(>|t|)   
#> (Intercept)        3369.53    1181.28    2.85   0.0069 **
#> `number of stops`     2.87      11.24    0.26   0.7995   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 4300 on 39 degrees of freedom
#> Multiple R-squared:  0.00167,    Adjusted R-squared:  -0.0239 
#> F-statistic: 0.0654 on 1 and 39 DF,  p-value: 0.8

Research question 2

Research question 2

Research question 3

#> 
#> Call:
#> lm(formula = log(`Total crimes`) ~ log(`Non-Family Households`) + 
#>     I(`Number of people Poverty`^2), data = crime_demostats_2014_2016_2)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2.2110 -0.4245 -0.0208  0.3998  1.8285 
#> 
#> Coefficients:
#>                                  Estimate Std. Error t value
#> (Intercept)                     -1.23e+00   5.88e-01   -2.10
#> log(`Non-Family Households`)     9.73e-01   6.79e-02   14.32
#> I(`Number of people Poverty`^2)  1.18e-08   3.69e-09    3.21
#>                                 Pr(>|t|)    
#> (Intercept)                       0.0382 *  
#> log(`Non-Family Households`)      <2e-16 ***
#> I(`Number of people Poverty`^2)   0.0017 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.736 on 117 degrees of freedom
#> Multiple R-squared:  0.732,  Adjusted R-squared:  0.728 
#> F-statistic:  160 on 2 and 117 DF,  p-value: <2e-16

Conclusion